Denoising Reveals Low-Occupancy Populations in Protein Crystals

Advances in structural biology increasingly focus on uncovering protein dynamics and transient or weak macromolecular complexes. Such studies require modeling of low-occupancy species, for instance time-evolving intermediates and bound ligands. In protein crystallography, difference maps that compare paired perturbed and reference datasets are a powerful way to identify and aid modeling of low-occupancy species. Current methods to generate difference maps, however, rely on manually tuned parameters and, in cases of weak signal due to low occupancy, can fail to extract clear, chemically interpretable signals. We address these issues, first by showing negentropy is an effective metric to assess difference map quality and can therefore be used to automatically determine parameters needed during difference map calculation. Leveraging this, we apply total variation denoising, an image restoration technique that requires a choice of regularization parameter, to crystallographic difference maps. We show that total variation denoising improves map signal-to-noise and enables us to estimate the latent phase contribution of low-occupancy states. This technology opens new possibilities for time-resolved and ligand-screening crystallography in particular, allowing the detection of states that previously could not be resolved due to their inherently low occupancy.